Application of artificial intelligence and machine learning in early detection of adverse drug reactions (ADRs) and drug-induced toxicity

Abstract

Adverse drug reactions (ADRs) and drug-induced toxicity are major challenges in drug discovery, threatening patient safety and dramatically increasing healthcare expenditures. Since ADRs and toxicity are not as visible as infectious diseases, the potential consequences are considerable. Early detection of ADRs and drug-induced toxicity is an essential indicator of a drug's viability and safety profile. The introduction of artificial intelligence (AI) and machine learning (ML) approaches has resulted in a paradigm shift in the field of early ADR and toxicity detection. The application of these modern computational methods allows for the rapid, thorough, and precise prediction of probable ADRs and toxicity even before the drug’s practical synthesis as well as preclinical and clinical trials, resulting in more efficient and safer medications with a lesser chance of drug’s withdrawal. This present review offers an in-depth examination of the role of AI and ML in the early detection of ADRs and toxicity, incorporating a wide range of methodologies ranging from data mining to deep learning followed by a list of important databases, modeling algorithms, and software that could be used in modeling and predicting a series of ADRs and toxicity. This review also provides a complete reference to what has been performed and what might be accomplished in the field of AI and ML-based early identification of ADRs and drug-induced toxicity. By shedding light on the capabilities of these technologies, it highlights their enormous potential for revolutionizing drug discovery and improving patient safety.